Mass Spectrometry Imaging for Clinical Diagnosis and Prognosis of Human Cancers

质谱成像用于人类癌症的临床诊断和预后

• 批准号: 9191650
• 负责人: Livia Schiavinato Eberlin
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9191650
• 关键词: Achievement; Air; American; Analytical Chemistry; Animal Model; Area; Award; Biological; Biomedical Research; Biometry; Biostatistical Methods; Cancer Biology; Cancer Diagnostics; Cancer Patient; Cancer Prognosis; Cancerous; Carcinoma in Situ; Cells; Charge; Chemicals; Chemistry; Citric Acid Cycle; Clinic; Clinical; Clinical Data; Clinical Sciences; Complex; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Doctor of Philosophy; Electrospray Ionization; Environment; Evaluation; Excision; Experimental Models; Fatty Acids; Gene Expression; Genes; Goals; Health; Human; Image; Image Analysis; Imaging Techniques; In Situ; Lasso; Link; Lipids; MYC gene; Malignant Neoplasms; Manuscripts; Maps; Mass Spectrum Analysis; Measures; Medicine; Mentors; Mentorship; Metabolic; Metabolic Pathway; Metabolism; Methods; Microarray Analysis; Molecular; Molecular Biology; Molecular Profiling; Mus; Oncogenes; Operative Surgical Procedures; Pattern; Peer Review; Phase; Play; Policy Research; Primary carcinoma of the liver cells; Procedures; Process; Publishing; Regulation; Research; Research Personnel; Research Project Grants; Research Training; Role; Sampling; Sensitivity and Specificity; Societies; Solid; Solid Neoplasm; Solvents; Specimen; Spectrometry, Mass, Electrospray Ionization; Speed; Statistical Methods; Surface; Surgical margins; System; Technology; Testing; Time; Tissue Sample; Tissues; Training; Transgenic Mice; Translating; Translational Research; Universities; Work; aerobic glycolysis; base; cancer diagnosis; cancer type; career; career development; clinical Diagnosis; clinical practice; cooking; experience; glucose transport; innovation; interest; ionization; ionization technique; malignant stomach neoplasm; mass spectrometer; mouse model; new technology; novel; oncology; outcome forecast; overexpression; prognostic; proteomic signature; research study; statistics; success; tool; tumor; tumorigenesis

DESCRIPTION (provided by applicant): There is a current need in the clinical sciences for new technologies to rapidly diagnose cancers based on the detection of dysregulated molecular signatures. Abnormal expression of small metabolites involved in key steps of glucose transport, glutaminolysis, aerobic glycolysis and Krebs cycle, and larger metabolites such as fatty acids and complex lipids has been observed in various types of cancers. Moreover, abnormal metabolic patterns have been associated with specific genes linked to cancer prognosis. The MYC oncogene, for example, which is amplified in 55% of human hepatocellular carcinomas (HCC), is known to play a key role in the regulation of metabolic pathways and has also been associated with poor prognosis. Hence, the ability to rapidly and easily measure metabolites could provide a powerful approach for the clinical diagnosis and prognosis of cancers. New ambient ionization mass spectrometry imaging (MSI) techniques can perform direct analysis of tissue samples for in situ, near real time assessment of their molecular signatures. The goal of this proposal is to develop an ambient ionization MSI technique, desorption electrospray ionization (DESI-MSI), in conjunction with biostatistical tools to measure, define and validate metabolic signatures that are diagnostic and prognostic of human solid cancers, and to test this technology as a clinical tool for intrasurgical diagnosis of cancers. Application of DESI-MSI to analyze human cancerous tissue is a recent line of research developed in the last 6 years, in which I played a leading role during my PhD with Prof. R. Graham Cooks at Purdue University, and that I continue to develop in my postdoctoral research with Prof. Richard N. Zare at Stanford University. DESI-MSI allows hundreds of metabolites to be measured, imaged and accurately identified from an unmodified tissue sample in less than a second per pixel, in the open air, ambient environment. Although powerful, the DESI-MSI experiment is fairly simple: a spray of charged droplets extract metabolites from a sample surface, and are captured by a mass spectrometer for chemical analysis and identification. I believe this technology has the potential to transform the way cancer is diagnosed and treated in the clinical setting. The specific aims of my K99/R00 proposal are: 1. Develop DESI-MSI and refined statistical tools to identify and validate metabolic signatures diagnostic of a solid tumor, human HCC, 2. Investigate if certain metabolic patterns are related with a specific gene, the MYC oncogene, using the refined transgenic mouse models of MYC-induced HCC, 3. Evaluate DESI-MSI as a clinical tool for intrasurgical diagnosis and prognosis of HCC and other solid human tumors. While the initial aims of this proposal are focused on HCC, the developed methods will be applicable to study other human solid cancer, as it will be pursued in my independent phase, and thus have broad significance in human cancer diagnosis, prognosis and treatment. I have strong expertise in analytical chemistry and mass spectrometry, and a track-record of success in developing novel mass spectrometry tools for biological sample analysis. I have published 36 peer-reviewed manuscripts and have been honored to receive few awards for my research achievements, including the Nobel Laureate Signature Award of the American Chemical Society in recognition as 2012's best doctoral dissertation amongst all branches of Chemistry in the USA. However, while my prior research and training experiences in MS and translational research have enabled me to conduct the MS and clinical portions of collaborative research projects, through my interdisciplinary interactions as a postdoctoral researcher at Stanford University I recognized that my chemical training is not sufficient to conduct significant biomedical research as an independent researcher. Prior to engaging in a career as an independent investigator in cancer/biomedical research, I would greatly benefit from training in high-dimensional statistics methods to properly analyze and interpret mass spectral data of clinical samples, and in basic molecular biology methods for understanding cancer biology processes. Stanford University provides a spectacular environment to pursue the interdisciplinary project I propose and to receive training from the most outstanding researchers in these areas. As part of my career development, my mentorship and training will be provided by Prof. Richard N. Zare (Department of Chemistry), an innovator in methods of chemical analysis, Prof. Robert Tibshirani (Departments of Health Research & Policy, and Statistics), famous for the development of biostatistical methods for high-dimensional data analysis, and Prof. Dean Felsher (Department of Medicine, Division of Oncology), a pioneer in the development of MYC-induced transgenic mouse models of cancers. The training will be achieved through experimental work and also formal course work. My long-term career goal as an independent researcher is to develop novel MS technology for clinical diagnosis and prognosis of human cancers. As an independent researcher, I will apply my expertise in MS, and the training in biostatistics and molecular biology that I will receive through the K99 period to develop new, automated MS tools for clinical and intrasurgical diagnosis and prognosis of various human cancers, and to translate this technology to the clinics. I have a particular interest in using MS for assessing cancer margins during surgical resection, procedure for which new and rapid diagnostic methods are greatly needed. Aims 1 and 2 will be performed during the K99 mentored phase, and aim 3 will be pilot for HCC and much further explored in the R00 independent phase for other solid human cancers. The K99/R00 award will support my development into an independent investigator who develops relevant novel mass spectrometry tool in combination with biostatistical methods for clinical diagnosis and prognosis of human cancers.

描述（由申请人提供）：目前临床科学需要基于检测失调分子特征的新技术来快速诊断癌症。参与葡萄糖转运、谷氨酰胺解、有氧糖酵解和克雷布斯循环关键步骤的小代谢物和大代谢物（如脂肪酸和复杂脂质）的异常表达已在各种类型的癌症中观察到。此外，异常的代谢模式与癌症预后相关的特定基因有关。例如，MYC癌基因在55%的人肝细胞癌（HCC）中扩增，已知在代谢途径的调节中起关键作用，也与预后不良有关。因此，快速方便地测量代谢物的能力可以为癌症的临床诊断和预后提供强有力的方法。新的环境电离质谱成像（MSI）技术可以对组织样品进行直接的原位分析，近乎实时地评估其分子特征。本建议的目标是开发一种环境电离MSI技术，解吸电喷雾电离（DESI-MSI），结合生物统计学工具来测量、定义和验证用于诊断和预测人类实体癌的代谢特征，并测试该技术作为手术内诊断癌症的临床工具。应用DESI-MSI分析人类癌组织是近6年来发展起来的一项新研究，我在普渡大学攻读博士学位期间与R. Graham Cooks教授一起担任主导角色，并在斯坦福大学与Richard N. Zare教授一起进行博士后研究。DESI-MSI可以在露天环境中，以每像素不到一秒的时间，从未经修饰的组织样本中测量、成像和准确识别数百种代谢物。尽管功能强大，DESI-MSI实验相当简单：带电液滴的喷雾从样品表面提取代谢物，并被质谱仪捕获，用于化学分析和鉴定。我相信这项技术有潜力改变临床诊断和治疗癌症的方式。我的K99/R00提案的具体目标是：1。开发DESI-MSI和改进的统计工具，以识别和验证实体肿瘤（人类HCC）的代谢特征诊断，2。利用改良的MYC诱导的肝癌转基因小鼠模型，研究某些代谢模式是否与特定基因MYC致癌基因相关。评价DESI-MSI作为HCC和其他实体人肿瘤术中诊断和预后的临床工具。虽然本课题的初始目标是针对HCC，但所开发的方法将适用于其他人类实体癌的研究，这将是我独立阶段的研究方向，对人类癌症的诊断、预后和治疗具有广泛的意义。我在分析化学和质谱方面有很强的专业知识，并在开发用于生物样品分析的新型质谱工具方面取得了成功。我发表了36篇同行评议的手稿，并因我的研究成果而荣幸地获得了一些奖项，包括美国化学学会的诺贝尔奖得主签名奖，被认为是2012年美国化学所有分支中最好的博士论文。然而，虽然我之前在MS和转化研究方面的研究和培训经历使我能够进行合作研究项目的MS和临床部分，但通过我在斯坦福大学博士后期间的跨学科互动，我认识到我的化学训练不足以作为独立研究人员进行重要的生物医学研究。在从事癌症/生物医学研究的独立研究者之前，我将从高维统计方法的培训中受益匪浅，以正确分析和解释临床样品的质谱数据，以及基本的分子生物学方法，以了解癌症生物学过程。斯坦福大学为我提出的跨学科项目提供了一个绝佳的环境，并接受了这些领域最杰出的研究人员的培训。作为我职业发展的一部分，我的指导和培训将由化学分析方法的创新者Richard N. Zare教授（化学系），以高维数据分析的生物统计方法的发展而闻名的Robert Tibshirani教授（卫生研究与政策和统计学系）和Dean Felsher教授（肿瘤学系医学系）提供，他是开发myc诱导的转基因癌症小鼠模型的先驱。培训将通过实验工作和正式的课程工作来实现。作为一名独立研究人员，我的长期职业目标是开发用于人类癌症临床诊断和预后的新型MS技术。作为一名独立研究人员，我将运用我在MS方面的专业知识，以及我在K99期间接受的生物统计学和分子生物学方面的培训，开发新的自动化MS工具，用于临床和手术内诊断和各种人类癌症的预后，并将这项技术转化为临床。我对在手术切除过程中使用MS评估肿瘤边缘特别感兴趣，这一过程非常需要新的快速诊断方法。目标1和目标2将在K99指导阶段进行，目标3将作为HCC的试点，并在R00独立阶段进一步探索其他实体人类癌症。K99/R00奖将支持我发展成为一名独立的研究者，开发相关的新型质谱工具，结合生物统计学方法，用于人类癌症的临床诊断和预后。